Abstract
Electronic health record (EHR) analysis has become increasingly important in improving the quality of human healthcare. To leverage the full insights from the big EHRs, it is very important to define some application scenarios for which the relevant data can be extracted for training machine learning models to accomplish the expected goals. In this paper, we develop a system on how to recommend medical treatment solutions for patients living in the countryside and small cities when they happen to have schizophrenia but the doctors in the local hospitals do not have sufficient expertise to deal with such challenges. In the EHRs, we take the patients’ symptom descriptions as documents and then develop NLP and unsupervised machine learning techniques to analyze such documents to find the relevant and effective treatment solutions provided by medical experts. Extensive experimental results with different vector representations for documents show that the binary keyword vector representation works best to find relevant and effective medical treatment plans and solutions from the EHRs for any input symptom description.
Highlights
In China, the medical resources are distributed very unevenly
We develop NLP and machine learning techniques to analyze patients’ Electronic health record (EHR) in our cloud to find the available relevant and effective treatment solutions provided by medical experts in big hospitals according to the input patient’s symptom description
For optimizing the word2Vec, we have investigated 4 models: continuous bag-ofwords (CBOW) with hierarchical Softmax, CBOW with negative sampling, skip-gram with hierarchical sampling, and skipgram with negative sampling
Summary
In China, the medical resources are distributed very unevenly. Most of the excellent medical resources are concentrated on the big hospitals in big cities, whereas the countryside and small cities are allocated with a small portion of the medical resources. The quality is far from being satisfactory, but most of the country’s population is living there As a result, it is often very difficult for patients not living in big cities to receive the in-time effective medical treatments when they happen to have some schizophrenia due to the lack of high-quality medical resources. The successful development of such technologies can mitigate the lack of medical resources in the whole country and helps save patients’ lives, reduces the worries of patients’ families to some extent, and reduces patients’ costs by leveraging the insights from big EHRs. In this paper, we develop NLP and machine learning techniques to analyze patients’ EHRs in our cloud to find the available relevant and effective treatment solutions provided by medical experts in big hospitals according to the input patient’s symptom description. The successful development of this system can benefit doctors in big hospitals when they have patients who happen to have schizophrenia, offering the potential of all weather sharing of the medical resources in the whole country
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